1 syntax = "proto3"; 2 3 package tensorflow; 4 option cc_enable_arenas = true; 5 option java_outer_classname = "TensorProtos"; 6 option java_multiple_files = true; 7 option java_package = "org.tensorflow.framework"; 8 9 import "tensorflow/core/framework/resource_handle.proto"; 10 import "tensorflow/core/framework/tensor_shape.proto"; 11 import "tensorflow/core/framework/types.proto"; 12 13 // Protocol buffer representing a tensor. 14 message TensorProto { 15 DataType dtype = 1; 16 17 // Shape of the tensor. TODO(touts): sort out the 0-rank issues. 18 TensorShapeProto tensor_shape = 2; 19 20 // Only one of the representations below is set, one of "tensor_contents" and 21 // the "xxx_val" attributes. We are not using oneof because as oneofs cannot 22 // contain repeated fields it would require another extra set of messages. 23 24 // Version number. 25 // 26 // In version 0, if the "repeated xxx" representations contain only one 27 // element, that element is repeated to fill the shape. This makes it easy 28 // to represent a constant Tensor with a single value. 29 int32 version_number = 3; 30 31 // Serialized raw tensor content from either Tensor::AsProtoTensorContent or 32 // memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation 33 // can be used for all tensor types. The purpose of this representation is to 34 // reduce serialization overhead during RPC call by avoiding serialization of 35 // many repeated small items. 36 bytes tensor_content = 4; 37 38 // Type specific representations that make it easy to create tensor protos in 39 // all languages. Only the representation corresponding to "dtype" can 40 // be set. The values hold the flattened representation of the tensor in 41 // row major order. 42 43 // DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll 44 // have some pointless zero padding for each value here. 45 repeated int32 half_val = 13 [packed = true]; 46 47 // DT_FLOAT. 48 repeated float float_val = 5 [packed = true]; 49 50 // DT_DOUBLE. 51 repeated double double_val = 6 [packed = true]; 52 53 // DT_INT32, DT_INT16, DT_INT8, DT_UINT8. 54 repeated int32 int_val = 7 [packed = true]; 55 56 // DT_STRING 57 repeated bytes string_val = 8; 58 59 // DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real 60 // and imaginary parts of i-th single precision complex. 61 repeated float scomplex_val = 9 [packed = true]; 62 63 // DT_INT64 64 repeated int64 int64_val = 10 [packed = true]; 65 66 // DT_BOOL 67 repeated bool bool_val = 11 [packed = true]; 68 69 // DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real 70 // and imaginary parts of i-th double precision complex. 71 repeated double dcomplex_val = 12 [packed = true]; 72 73 // DT_RESOURCE 74 repeated ResourceHandleProto resource_handle_val = 14; 75 76 // DT_VARIANT 77 repeated VariantTensorDataProto variant_val = 15; 78 79 // DT_UINT32 80 repeated uint32 uint32_val = 16 [packed = true]; 81 82 // DT_UINT64 83 repeated uint64 uint64_val = 17 [packed = true]; 84 }; 85 86 // Protocol buffer representing the serialization format of DT_VARIANT tensors. 87 message VariantTensorDataProto { 88 // Name of the type of objects being serialized. 89 string type_name = 1; 90 // Portions of the object that are not Tensors. 91 bytes metadata = 2; 92 // Tensors contained within objects being serialized. 93 repeated TensorProto tensors = 3; 94 } 95
